TensorFlow 1 version | View source on GitHub |
Depthwise separable 2D convolution.
tf.keras.layers.SeparableConv2D(
filters, kernel_size, strides=(1, 1), padding='valid', data_format=None,
dilation_rate=(1, 1), depth_multiplier=1, activation=None, use_bias=True,
depthwise_initializer='glorot_uniform', pointwise_initializer='glorot_uniform',
bias_initializer='zeros', depthwise_regularizer=None,
pointwise_regularizer=None, bias_regularizer=None, activity_regularizer=None,
depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None,
**kwargs
)
Separable convolutions consist in first performing
a depthwise spatial convolution
(which acts on each input channel separately)
followed by a pointwise convolution which mixes together the resulting
output channels. The depth_multiplier
argument controls how many
output channels are generated per input channel in the depthwise step.
Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block.
Arguments | |
---|---|
filters
|
Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). |
kernel_size
|
An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions. |
strides
|
An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the height and width.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any dilation_rate value != 1.
|
padding
|
one of "valid" or "same" (case-insensitive).
|
data_format
|
A string,
one of channels_last (default) or channels_first .
The ordering of the dimensions in the inputs.
channels_last corresponds to inputs with shape
(batch, height, width, channels) while channels_first
corresponds to inputs with shape
(batch, channels, height, width) .
It defaults to the image_data_format value found in your
Keras config file at ~/.keras/keras.json .
If you never set it, then it will be "channels_last".
|
dilation_rate
|
An integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any dilation_rate value != 1 is
incompatible with specifying any strides value != 1.
|
depth_multiplier
|
The number of depthwise convolution output channels
for each input channel.
The total number of depthwise convolution output
channels will be equal to filters_in * depth_multiplier .
|
activation
|
Activation function to use.
If you don't specify anything, no activation is applied
(ie. "linear" activation: a(x) = x ).
|
use_bias
|
Boolean, whether the layer uses a bias vector. |
depthwise_initializer
|
Initializer for the depthwise kernel matrix. |
pointwise_initializer
|
Initializer for the pointwise kernel matrix. |
bias_initializer
|
Initializer for the bias vector. |
depthwise_regularizer
|
Regularizer function applied to the depthwise kernel matrix. |
pointwise_regularizer
|
Regularizer function applied to the pointwise kernel matrix. |
bias_regularizer
|
Regularizer function applied to the bias vector. |
activity_regularizer
|
Regularizer function applied to the output of the layer (its "activation").. |
depthwise_constraint
|
Constraint function applied to the depthwise kernel matrix. |
pointwise_constraint
|
Constraint function applied to the pointwise kernel matrix. |
bias_constraint
|
Constraint function applied to the bias vector. |
Input shape:
4D tensor with shape:
(batch, channels, rows, cols)
if data_format='channels_first'
or 4D tensor with shape:
(batch, rows, cols, channels)
if data_format='channels_last'.
Output shape:
4D tensor with shape:
(batch, filters, new_rows, new_cols)
if data_format='channels_first'
or 4D tensor with shape:
(batch, new_rows, new_cols, filters)
if data_format='channels_last'.
rows
and cols
values might have changed due to padding.